Self-Adaptive Systems and Applications

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https://hdl.handle.net/10125/102625

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    Dynamic Interest Points: A Formalism to Identify Areas to Patrol within a Continuous Environment
    (2023-01-03) Chahal, Jamy; Belbachir, Assia; El Fallah Seghrouchni, Amal
    The multi-agent patrolling problem consists of positioning agents to minimize the idleness, which represents the time difference between two visits of a same location by at least one agent.In the literature, these locations are defined manually by setting static nodes within a graph representation. However, in the context of patrolling a continuous environment, using static nodes cannot guarantee the coverage of the whole environment. In this article, we propose to discretize the continuous environment in order to generate dynamic waypoints called interest points (IP). We prove that these dynamic IP guarantee the coverage of the whole environment while dealing with its topography and the agent's observation range. We evaluated and compared our approach by benchmarking patrolling environment dealing with different observation ranges. Experiments show that dynamic IP locations are adaptive and more efficient to locate high idleness areas compared to static IP approach.
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    Software Technology to Develop Large-Scale Self-Adaptive Systems: Accelerating Agent-Based Models and Fuzzy Cognitive Maps via CUDA
    (2023-01-03) Ghumrawi, Kareem; Ha, Kim; Beerman, Jack; Rudie, John-David; Giabbanelli, Philippe
    Agent-Based Models (ABMs) have long served to study self-adaptive systems and the emergence of population-wide patterns from simple rules applied to individuals. Recently, the rules for each agent have been expressed using a Fuzzy Cognitive Map (FCM), which is elicited from a subject-matter expert. This provides a transparent and participatory process to externalize the `mental model' of an expert and directly embed it into agents. However, software technology has been lacking to support such hybrid ABM/FCM models at scale, which has drastically limited the scope of applications and the ability of researchers to study emergent phenomena over large populations. In this paper, we designed and implemented the first open-source library that automatically accelerates ABM/FCM models by leveraging the CUDA cores available in a Graphical Processing Unit. We demonstrate the correctness and scaling of our library on a case study as well as across different networks representing agent interactions.
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    Introduction to the Minitrack on Self-Adaptive Systems and Applications
    (2023-01-03) Brehler, Marius; Detzner, Peter; Pecorella, Tommaso; Kerner, Sören
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    An Architectural Design to Address the Impact of Adaptations on Intrusion Detection Systems
    (2023-01-03) Riley, Ian; Marshall, Allen; Quirk, Logan; Gamble, Rose
    Many self-adaptive, autonomous systems rely on component technologies to report anomalies to planning processes that can choose adaptations. What if the analysis technologies themselves need to be adapted? We consider an intrusion detection system (IDS) supported by two component technologies that assist its decision making: a neural network that finds security anomalies and an attack graph that informs the IDS about system states of interest. The IDS’s purpose is to send alerts regarding security anomalies. Planning processes respond to alerts by selecting mitigation strategies. Mitigations are imposed system-wide and can result in adaptations to the analysis technology, such as the IDS. Thus, without adaptation it may reach a state of stagnation in its detection quality. In this paper, we describe an architectural design for an adaptive layer that works directly with an IDS. We examine two use cases involving different mitigation strategies and their impact on the IDS’s supporting components.